Tag Archives: cloud integration platform
The emergence of the business cloud is making the need for data ever more prevalent. Whatever your business, if your role is in the sales, marketing or service departments, chances are your productivity depends a great deal on the ability to move data quickly in and out of Salesforce and its ecosphere of applications.
With the in-built data transformation intelligence, the Data Wizard (click here to try the Beta version), changes the landscape of what traditional data loaders can do. The Data Wizard takes care of the following aspects, so that you don’t have to:
- Data Transformations: We built in over 300 standard data transformations so you don’t have to format the data before bringing it in (eg. combining first and last names into full names, adding numeric columns for totals, splitting address fields into its separate components).
- Built-in intelligence: We automate the mapping of data into Salesforce for a range of common use cases (eg., Automatically mapping matching fields, intelligently auto-generating date format conversions , concatenating multiple fields).
- App-to-app integration: We incorporated pre-built integration templates to encapsulate the logic required for integrating Salesforce with other applications (eg., single click update of customer addresses in a Cloud ERP application based on Account addresses in Salesforce) .
Unlike the other data loading apps out there, the Data Wizard doesn’t presuppose any technical ability on the part of the user. It was purpose-built to solve the needs of every type of user, from the Salesforce administrator to the business analyst.
Despite the simplicity the Data Wizard offers, it is built on the robust Informatica Cloud integration platform, providing the same reliability and performance that is key to the success of Informatica Cloud’s enterprise customers, who integrate over 5 billion rows of data per day. We invite you to try the Data Wizard for free, and contribute to the Beta process by providing us with your feedback.
A lot of the trends we are seeing in enterprise integration today are being driven by the adoption of cloud based technologies from IaaS, PaaS and SaaS. I just was reading this story about a recent survey on cloud adoption and thought that a lot of this sounds very similar to things that we have seen before in enterprise IT.
Why discuss this? What can we learn? A couple of competing quotes come to mind.
Those who forget the past are bound to repeat it. – Edmund Burke
We are doomed to repeat the past no matter what. – Kurt Vonnegut
While every enterprise has to deal with their own complexities there are several past technology adoption patterns that can be used to drive discussion and compare today’s issues in order to drive decisions in how a company designs and deploys their current enterprise cloud architecture. Flexibility in design should be a key goal in addition to satisfying current business and technical requirements. So, what are the big patterns we have seen in the last 25 years that have shaped the cloud integration discussion?
1. 90s: Migration and replacement at the solution or application level. A big trend of the 90s was replacing older home grown systems or main frame based solutions with new packaged software solutions. SAP really started a lot of this with ERP and then we saw the rise of additional solutions for CRM, SCM, HRM, etc.
This kept a lot of people that do data integration very busy. From my point of view this era was very focused on replacement of technologies and this drove a lot of focus on data migration. While there were some scenarios around data integration to leave solutions in place these tended to be more in the area of systems that required transactional integrity and high level of messaging or back office solutions. On the classic front office solutions enterprises in large numbers did rip & replace and migration to new solutions.
2. 00s: Embrace and extend existing solutions with web applications. The rise of the Internet Browser combined with a popular and powerful standard programming language in Java shaped and drove enterprise integration in this time period. In addition, due to many of the mistakes and issues that IT groups had in the 90s there appeared to be a very strong drive to extend existing investments and not do rip and replace. IT and businesses were trying to figure out how to add new solutions to what they had in place. A lot of enterprise integration, service bus and what we consider as classic application development and deployment solutions came to market and were put in place.
3. 00s: Adoption of new web application based packaged solutions. A big part of this trend was driven by .Net & Java becoming more or less the de-facto desired language of enterprise IT. Software vendors not on these platforms were for the most part forced to re-platform or lose customers. New software vendors in many ways had an advantage because enterprises were already looking at large data migration to upgrade the solutions they had in place. In either case IT shops were looking to be either a .Net or Java shop and it caused a lot of churn.
4. 00s: First generation cloud applications and platforms. The first adoption of cloud applications and platforms were driven by projects and specific company needs. From Salesforce.com being used just for sales management before it became a platform to Amazon being used as just a run-time to develop and deploy applications before it became a full scale platform and an every growing list of examples as every vendor wants to be the cloud platform of choice. The integration needs originally were often on the light side because so many enterprises treated it as an experiment at first or a one off for a specific set of users. This has changed a lot in the last 10 years as many companies repeated their on premise silo of data problems in the cloud as they usage went from one cloud app to 2, 5, +10, etc. In fact, if you strip away where a solution happens to be deployed (on prem or cloud) the reality is that if an enterprise had previously had a poorly planned on premise architecture and solution portfolio they probably have just as poorly planned cloud architecture solution and portfolio. Adding them together just leads to disjoint solutions that are hard to integrate, hard to maintain and hard to evolve. In other words the opposite of the being flexible goal.
5. 10s: Consolidation of technology and battle of the cloud platforms. It appears we are just getting started in the next great market consolidation and every enterprise IT group is going to need to decide their own criteria for how they balance current and future investments. Today we have Salesforce, Amazon, Google, Apple, SAP and a few others. In 10 years some of these will either not exist as they do today or be marginalized. No one can say which ones for sure and this is why prioritizing flexibility in terms or architecture for cloud adoption.
For me the main take aways from the past 25 years of technology adoption trends for anyone that thinks about enterprise and data integration would be the following.
a) It’s all starts and ends with data. Yes, applications, process, and people are important but it’s about the data.
b) Coarse grain and loosely coupled approaches to integration are the most flexible. (e.g. avoid point to point at all costs)
c) Design with the knowledge of what data is critical and what data might or should be accessible or movable
d) Identify data and applications that might have to stay where it is no matter what.(e.g. the main frame is never dying)
e) Make sure your integration and application groups have access to or include someone that understand security. While a lot of integration developers think they understand security it’s usually after the fact that you find out they really do not.
So, it’s possible to shape your cloud adoption and architecture future by at least understanding how past technology and solution adoption has shaped the present. For me it is important to remember it is all about the data and prioritizing flexibility as a technology requirement at least at the same level as features and functions. Good luck.